Forecasting US GNP Growth: The Role of Uncertainty
Mawuli Segnon (),
Rangan Gupta (),
Stelios Bekiros () and
Mark Wohar ()
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Mawuli Segnon: Department of Economics, University of Münster, Germany
No 201667, Working Papers from University of Pretoria, Department of Economics
There are a large number of models developed in the literature to analyse and forecast the changes in output dynamics. The objective of this paper is to compare the forecasting ability of uni- and bivariate models in terms of forecasting U.S. GNP growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as the superior predictive ability (SPA) and equal accuracy tests, we evaluate the forecasting performance of our models over the quarterly period of 1919:2-2014:4, given an in-sample of 1900:1 1919:1. We find that the economic policy uncertainty index should be improving the accuracy of U.S. GNP growth forecasts in the bivariate models. While we find that the Markov switching time varying parameter VAR (MS-TVP-VAR) models in most cases cannot be outperformed its competitive models according to the root mean squared error (RMSE), the density forecast measure reveals that the Bayesian VAR (BVAR) model with stochastic volatility in most cases is the model that produces more accurate forecasts. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rate.
Keywords: Forecast comparison; vector autoregressive models; US GNP; Economic Policy Uncertainty (search for similar items in EconPapers)
JEL-codes: C22 C32 E32 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for, nep-mac, nep-ore and nep-sog
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